Supervised Denoising for Extreme Low-Light Raw Videos

Denoising is a critical task in computer vision tasks, especially in challenging environments like extreme low-light conditions. However, the lack of research on denoising raw video in extreme low-light environments is notable, as is the absence of datasets specifically designed for this purpose. The primary challenge in denoising lies in balancing noise removal with detail preservation. Excessive denoising can cause the loss of fine details, while the insufficient denoising fails to adequately suppress noise, leading to degraded performance in downstream tasks such as object detection and recognition [1]. To address these limitations, we present a novel raw video dataset consisting of noisy-clean paired sequences captured under extreme low-light conditions, featuring diverse scenes and extended frames. In addition, we propose an efficient denoising framework tailored for this challenging scenario. Our approach combines shallow denoising, deformable convolution-based temporal alignment, and spatiotemporal attention to reduce noise while preserving texture and temporal consistency effectively. A texture-preserving loss is also proposed to prevent over-smoothing and retain fine details. Our proposed method outperforms state-of-the-art denoising models in terms of PSNR and SSIM on both synthetic and real-world extreme low-light videos, while exhibiting minimal side effects and preserving sharp details, as demonstrated by the quantitative results.